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MS analysis of microplastics extracted from the stomach content of benthivore fish from the Texas Gulf Coast
Marine Pollution Bulletin 137 (2018) 91–95
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Pyr-GC/MS analysis of microplastics extracted from the stomach content of benthivore fish from the Texas Gulf Coast
T
Colleen A. Petersa, , Erik Hendricksonb, Elizabeth C. Minorb, Kathryn Schreinerb, Julie Halburb, Susan P. Brattona ⁎
a b
Department of Environmental Science, Baylor University, Waco, TX 76798-7266, United States of America University of Minnesota, Duluth, United States of America
ARTICLE INFO
ABSTRACT
Keywords: Microplastic Polymer Pyr-GC/MS Fish Ingestion Texas
Fish ingestion of microplastic has been widely documented throughout freshwater, marine, and estuarine species. While numerous studies have quantified and characterized microplastic particles, analytical methods for polymer identification are limited. This study investigated the applicability of pyr-GC/MS for polymer identification of microplastics extracted from the stomach content of marine fish from the Texas Gulf Coast. A total of 43 microplastic particles were analyzed, inclusive of 30 fibers, 3 fragments, and 10 spheres. Polyvinyl chloride (PVC) and polyethylene terephthalate (PET) were the most commonly identified polymers (44.1%), followed by nylon (9.3%), silicone (2.3%), and epoxy resin (2.3%). Approximately 42% of samples could not be classified into a specific polymer class, due to a limited formation of pyrolytic products, low product abundance, or a lack of comparative standards. Diethyl phthalate, a known plasticizer, was found in 16.3% of the total sample, including PVC (14.3%), silicone (14.3%), nylon (14.3%), and sample unknowns (57.2%).
1. Introduction Microplastics are major global contaminants, ubiquitous throughout freshwater and marine systems (Eriksen et al., 2013; Lattin et al., 2004; Moore et al., 2011; Ng and Obbard, 2006; Sadri and Thompson, 2014). Due to their small size (i.e. < 5 mm), it is difficult to predict particle transport following release into aquatic systems, however, microplastics have been discovered within waters from the near shore to open ocean (Eriksen et al., 2014; Kang et al., 2015), from the surface to benthos (Song et al., 2014; Woodall et al., 2014), and from subtropical to polar seas (Law et al., 2010; Obbard et al., 2014). While the environmental impact of these contaminants is not fully understood, microplastic ingestion has been identified within taxa spanning from invertebrates to large marine mammals (Hurley et al., 2017; Taylor et al., 2016). Fish ingestion of microplastic has been confirmed within freshwater, marine, and estuarine species, ranging from a few percent to more than two-thirds of all fish examined (Lusher et al., 2013; Nadal et al., 2016; Peters and Bratton, 2016; Peters et al., 2017; Possatto et al., 2011; Romeo et al., 2015; Sanchez et al., 2014; Vendel et al., 2017). It is likely that variations in microplastic ingestion are the result of several factors, such as the species of examination, location of collection,
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methodologies employed for microplastic extraction, and the analytical analyses utilized for polymer identification. Due to the complex nature of these micro-contaminants, it is now becoming standard to employ two or more identification techniques for the confirmation of plastic. Initial identification routinely involves a physical characterization of the particle (e.g. size, morphology, and color), aided by microscopy, followed by a secondary identification via chemical characterization (e.g. spectroscopy) to identify the specific type of plastic polymer (Shim et al., 2017). Polymer characterization often employs Fourier transform infrared spectroscopy (FTIR) or Raman spectroscopy (Lenz et al., 2015; Shim et al., 2017). Both techniques use electromagnetic radiation to profile samples, however, FTIR is a measure of the particle's covalent chemical bonds using the absorbance of their vibrational modes while Raman is a measure of the particle molecular structure using light scattering from key vibrational modes after excitation with a visible light source (Käppler et al., 2016; Löder and Gerdts, 2015). While both techniques explore molecular vibrations, the differences in their approach mean that different types of bonds are highlighted by each technique. FTIR spectra highlight polar covalent bonds, while Raman spectra highlight more purely covalent bonds such as carbon to carbon (CeC) or sulfur to sulfur (SeS). FTIR is coupled with varying modes of measure (e.g.
Corresponding author at: Department of Environmental Science, Baylor University, One Bear Place #97266, Waco, TX 76798-7266, United States of America. E-mail address: [email protected] (C.A. Peters).
https://doi.org/10.1016/j.marpolbul.2018.09.049 Received 7 June 2018; Received in revised form 28 September 2018; Accepted 29 September 2018 0025-326X/ © 2018 Elsevier Ltd. All rights reserved.
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micro-FTIR, transmission, reflectance, and attenuated total reflectance), some of which can minimize method limitations such as the requirement of an extensive sample pretreatment and inhibited analysis of plastics which contain irregular surfaces, (Ng and Obbard, 2006; Song et al., 2014). While FTIR can analyze particles as small as 10 μm (the size of the IR beam aperture), particles of this size often require multiple analysis runs or produce unclear results, thus FTIR is most applicable for particles that are > 50μm (Shim et al., 2017). Comparatively, the laser aperture utilized in Raman spectroscopy is smaller than that of FTIR, thus it can identify particles as small as a few μm in size. Raman has been found to be sensitive to additive and pigment chemicals, which are often incorporated during the production phase of plastics, resulting in the interference in polymer identification (Lenz et al., 2015). FTIR may also be challenged by additives and plastic copolymers; work by Hendrickson et al., 2018 indicates that ATR-FTIR may mask chemical constituents within heterogeneous particles that appear when the particle undergoes pyr-GC/MS analysis. A third method of polymer identification is the coupling of thermal desorption or pyrolysis with gas chromatography–mass spectrometry or GC/MS (Dümichen et al., 2015; Frias et al., 2013). Pyrolysis-GC/MS (pyr-GC/MS) uses heat in an inert environment (i.e., no oxygen) to decompose polymeric material in a predictable fashion. The pieces of polymer generated can then be separated by gas chromatography on the basis of their size and polarity and analyzed by mass spectrometric detector at the outlet of the gas chromatography column. (Frias et al., 2013). This method yields a total chromatogram (abundance vs time) for the separated pyrolysis products and provides mass spectrometry data throughout the chromatogram as well. These can be compared against a known reference library to determine the specific class of polymer being analyzed. This method is beneficial over FTIR and Raman spectroscopy as it can characterize particles < 10μg when measured in splitless mode, and the utilization of a thermal analysis combined with GC/MS, enables the separation and analysis of chemical additives as well as the polymer material (Hendrickson et al., 2018). However, pyr-GC/MS is destructive, resulting in the total loss of the particle and subsequently eliminating further particle analysis. Despite recent advancements in these analytical methods as applied to microplastics, the applicability and feasibility of each is somewhat incomplete due to the wide range of microplastic polymers, including weathered polymers, and additives found throughout the environment. This research serves as one of the first applications of pyr-GC/MS for microplastic polymer identification within a fish ingestion study, and specifically investigates the polymer distribution of microplastic recovered from the stomach content of six marine fish species from the Texas Gulf Coast. The pyr-GC/MS method utilized within this study has previously been applied to the identification of microplastic recovered from the waters of Western Lake Superior (Hendrickson et al., 2018). The use of this method here enabled a comparison of polymer results between freshwater and marine systems and an investigation of method applicability across sample matrixes.
microbalance and particles < 10 μg were measured in splitless introduction into the gas chromatograph, while particles > 10 μg were introduced using a 1:100 split (Hendrickson et al., 2018). Samples were analyzed using an Agilent 7890B Gas Chromatograph with Agilent 5977A mass-selective detector (MSD) Mass Spectrometer and Gerstel Pyrolysis/Thermal Desorption Unit (Gerstel GmbH & Co. KG, Germany). All pyrolyzer and GC unit parameters adhered to the protocol of Hendrickson et al. (2018). The MSD utilized electron impact (EI+, 70 eV) for the ionization source and scanned for ions from m/z 10–550 (Hendrickson et al., 2018). Following analysis, ion chromatograms were assessed with the National Institute of Standards and Technology (NIST) mass spectra library (Version 2.0, 12/4/12, available through the mass spectrometer's software package) and the following standards: medium-density polyethylene (MDPE, catalog #: EV306010), polystyrene (PS, catalog #: ST316051), polyvinyl chloride (PVC, catalog #: CV316010), and polyethylene terephthalate (PET, catalog #: ES306030), all in powder form (250–350 μm) (Goodfellow, Inc.). Samples which yielded a low number of pyrolytic products (< 4 total) or low pyrolytic product abundances were evaluated via Mass Hunter qualitative analysis software which was utilized to integrate total ion chromatogram peak areas and calculate a 3:1 signal-to-noise ratio (Hendrickson et al., 2018). 3. Results A total of 43 microplastic samples were analyzed, inclusive of 30 fibers, 3 fragments, and 10 spheres (microbeads). Particles were identified into the following five polymer classes: PVC (Fig. 1; Table 1) and PET, constituting approximately 44.1% of the total sample, silicone (2.3%), epoxy resin (2.3%), and nylon (9.3%) (Fig. 2). Half of the nylon particles were further classified as Nylon 6 due to the high abundance of caprolactam within the pyrogram results (Lehrle et al., 2000). In addition to the five polymer classes, approximately 42% of particles were classified as sample unknowns, 21% of which displayed a similar chromatogram result inclusive of seven common pyrolytic products (i.e. Unknown Subsample A) (Fig. 3; Table 2). PVC polymers were inclusive of microplastic fibers (73.3%), fragments (20.0%), and spheres (6.7%), while all PET, epoxy resin, and nylon polymers were in the form of microplastic fibers and the single particle identified as silicone was in the form of a microbead. Particles classified as “Unknown” contained fiber (55.6%) and sphere (44.4%) morphologies (Fig. 4) and particles further categorized as “Unknown Subsample A” contained sphere (55.6%) and fiber (45.4%) morphologies. Diethyl phthalate was found in 16.3% of all particles analyzed, including PVC (14.3%), Silicone (14.3%), Nylon (14.3%), Unknown (28.6%), and Unknown Subsample A (28.6%). 4. Discussion Of the original marine microplastic data set (i.e. Peters et al., 2017), the most common particle morphologies were fibers (86.4%), followed by spheres (12.9%), and fragments (< 1.0%), thus, samples chosen for
2. Methods Microplastics were collected from the stomach content of 1381 marine fish, inclusive of six species (i.e. southern kingfish (Menticirrhus americanus), Atlantic croaker (Micropogonias undulates), Atlantic spadefish (Chaetodipterus faber), sand trout (Cynoscion arenarius), pinfish (Lagodon rhomboids), and grunt (Orthopristis chrysoptera) from the Texas Gulf Coast (Peters et al., 2017). Fish collection took place from September 2014 to September 2015, and stomach content analysis followed the protocol of Peters and Bratton (2016). Following identification, microplastics were characterized via particle size, morphology, and color (Peters et al., 2017). Approximately 5% of recovered microplastics were selected and transferred from Baylor to the University of Minnesota Duluth for pyrGC/MS analysis. Particle mass was measured via a Mettler Toledo XP2U
25000000 4.
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4.0 4.7 5.5 6.2 6.9 7.6 8.4 9.1 9.8 10.5 11.3 12.0 12.7 13.4 14.2 14.9 15.6 16.3 17.1 17.8 18.5 19.2 20.0 20.7 21.4
0 Retention Time
Fig. 1. Total chromatogram of PVC particle with pyrolytic products numerically labeled. 92
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Table 1 PVC pyrolytic products with retention times and parent m/z's. Sample Peak
Retention Time ( ± 0.5 min)
Pyrolytic Product
Parent m/z
1 2 3 4 5 6 7 8 9
4.4 6.5 7.2 9.5 12.1 13.1 14.8 16.9 20.2
Styrene Mesitylene Indene Naphthalene Biphenyl Acenaphthylene Diethyl phthalate Anthracene Pyrene
104 120 116 128 154 154 222 178 202
Fig. 4. Distribution of particle polymer classes and associated morphologies.
for analysis and the comparatively low proportion of microplastic fibers sampled. Previously, microplastic form has been utilized to hypothesize originating sources of pollution (e.g. fibers resulting from wastewater effluent), however, due to varying environmental conditions and a limited knowledge of microplastic transport pathways, it is difficult to assign microplastics to a single source. While pyr-GC/MS cannot distinguish originating sources of pollution, it can identify specific polymers and subsequently a measure of polymer distribution throughout the sample matrix. Polymer identification is also a first step toward predicting ultimate fate in an aquatic environment; for example, different plastic polymers have different susceptibilities to various biologically or photochemically mediated degradation processes (Gewert et al., 2015). Of the five polymer classes identified, PVC and PET were the most common, while all other polymer classes collectively constituted < 15% of the total sample. PVC is one of the leading polymers worldwide, accounting for 20% of all plastics manufacturing and prominent within applications for building, transport, and packaging, and as a strengthening coating for fabrics (British Plastics Federation, 2018). PET is the most common thermoplastic polymer resin of the polyester family and is widely utilized for packaging and bottle applications and for the production of synthetic fibers (i.e. polyester). Both PVC and PET are high density polymers (approximately 1.38 g/cm3), thus they are likely to sink within freshwater and marine habitats. As the fish species originally examined are benthivore foragers, the high proportion of PVC and PET within the sample is not unexpected. However, a study of microplastic pollution within Western Lake Superior identified PVC as the most common microplastic polymer collected within surface water samples and PET as the 4th most commonly identified polymer, suggesting that polymer density does not ultimately determine particle fate and transport (Hendrickson et al., 2018). In total, 42% of all particles analyzed could not be classified into a specific polymer class, due to either a lack of pyrolytic products formed, low pyrolytic product abundance, or the lack of a clear polymer match, and were subsequently categorized as “Unknown”. Fifty percent of unknown particles displayed a similar pyrogram result, suggesting a shared compositional origin, and were subsequently separately classified (i.e. Unknown Subsample A) despite the lack of polymer match. The particles classified as Unknown Subsample A were not cohesive in morphology, containing both spheres and fibers, which indicates a variation in origin. Additionally, half of the fibers within Unknown Subsample A exhibited diethyl phthalate, a known plasticizer, as a pyrolytic product. One possible hypothesis is that the particles of Unknown subsample A are associated with the petroleum industry (e.g. manufacturing waste or pitch byproducts or perhaps even products from plastics manufacturing, whose feedstock is usually from petroleum). The petroleum industry is highly concentrated along the Texas Gulf Coast and reflective of local land use patterns, such as high levels of local petroleum and natural gas industrialization and major petroleum transport zones (i.e. Port of Galveston). It is possible that particles with a sphere morphology are a direct result of the petroleum industry, while the fiber particles were in fact known plastic polymers which had
Fig. 2. Distribution of polymer classes identified via pyr-GC/MS. 6.
16000000 14000000
2.
Abundance
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. 8.
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5.
2000000 4.0 4.4 4.8 5.2 5.7 6.1 6.5 6.9 7.3 7.7 8.1 8.5 8.9 9.4 9.8 10.2 10.6 11.0 11.4 11.8 12.2 12.7 13.1 13.5 13.9 14.3 14.7
0
Retention Time
Fig. 3. Total chromatogram of Unknown Subsample A, a fiber particle with pyrolytic products numerically labeled. Table 2 Unknown Subsample A pyrolytic products with retention times and parent m/ z's. Pyrolytic products of peaks 1–7 are indicative of the particles within this classification. Peak number
Retention time ( ± 0.5 min)
Pyrolytic product
Parent m/z
1 2 3 4 5 6 7 8
6.2 7.7 8.5 8.7 10.1 10.9 12.2 14.7
Phenol 4‑Methylphenol 2,5‑Pyrrolidinedione Benzonitrile Benzenepropanenitrile Indole 3‑Methylindole Diethyl Phthalate
94 108 99 103 131 117 131 222
pyr-GC/MS analysis proportionally favored sphere and fragment morphologies, which were on average larger than microplastic fibers (Peters et al., 2017). Overall, the total number of samples analyzed was limited by time and monetary constraints, while the particles chosen for analysis were also limited by a minimum size constraint. These three factors contributed to the distribution of particle morphologies chosen 93
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been coated in or had absorbed waste products, subsequently inhibiting polymer identification. In addition to a high concentration of petroleum industrialization, major pollution events, such as the British Petroleum Deepwater Horizon oil spill, have impacted the Texas Gulf Coast (Nelson et al., 2015; Singleton et al., 2016). Studies investigating the impact of events, such as oil spills or petroleum waste contamination, cite inhalation, aspiration, ingestion via contaminated sediment, water, or prey, and absorption through the skin, as routes of exposure to aquatic organisms (NOAA, 2017). Laboratory studies investigating potential adverse effects, via the exposure of fish to oil contaminated sediment, indicate that fish experienced reduced growth (i.e. length and weight) and fecundity (Raimondo et al., 2016). Fish exposure to oil via contaminated water has also been linked to decreased swimming speeds, maximum metabolic rate, and aerobic scope (Stieglitz et al., 2016). While Peters et al. (2017) did not investigate adverse effects, the hypothesis of this study suggests that Texas Gulf Coast fish may experience adverse effects resulting from the ingestion of petroleum contaminated particles. A secondary hypothesis is that the particles of Unknown Subsample A are associated with coal tar, which is a liquid oil byproduct of coal pyrolysis and is utilized in its raw form for industries such as synthetic fiber, dyestuff, and coatings (Fardhyanti and Damayanti, 2015; Jiang et al., 2007). Major coal tar compound groups include: oxygen containing compounds (e.g. phenols), nitrogen containing compounds (e.g. amines), aromatic hydrocarbons (e.g. benzene hydrocarbons), and inorganic constituents (Fisher, 1938). Of the seven pyrolytic products indicative of Unknown Subsample A, phenol and 4-methylphenol (i.e. phenol group), and indole, 3-methylindole, and benzonitrile (i.e. amines) have been identified as constituents of coal tar. Additionally, diethyl phthalate was identified as a product of coal tar pyrolysis, suggesting that its presence does not always indicate a parent plastic polymer (Jiang et al., 2007). One primary use of coal tar is for the production of coal tar creosote (i.e. creosote), formed via the fractional distillation of crude coal tar (World Health Organization, 2004). Creosote is an oily liquid, consisting of aromatic hydrocarbons, anthracene, naphthalene, and phenanthrene derivatives and comprised of approximately 85% PAH's and 2–17% phenolics (U.S. Department of Health and Human Services, 2002). Similar to both coal tar and petroleum waste, creosote is a multicomponent mixture that varies per the source and preparation parameters utilized for production. Because of this, creosote components are rarely consistent per collective type and relative concentrations, however, display similar compositional chemical groups as that of coal tar. Creosote is utilized along the Texas Gulf coast as a wood preservative and water-proofing agent for marine pilings and as a preventative agent for plant and animal growth along concrete marine pilings (U.S. Department of Health and Human Services, 2002). Because of this, and similar to previous petroleum and coal tar hypotheses, creosote may serve as a vector for chemical pollution throughout Texas Gulf Coast waters and subsequently affect microplastic polymer identification. Although the hypotheses associating Unknown Subsample A with petroleum waste, coal tar, and creosote are preliminary, extensive research was conducted to investigate probable alternatives and none were readily identified. In addition to a literature search, the sample chromatogram results were compared against a NIST polymer library, and a reference text polymer library, inclusive of 163 standard polymer samples (Shin et al., 2011). Major groups represented within the library include: polyolefins, vinyl polymers with ethylene units, vinyl polymers with styrene units, vinyl polymers with styrene derivatives, acrylatetype polymers, chlorine-containing vinyl polymers, fluorine-containing vinyl polymers, diene-type elastomers, polyamides, polyacetals and polyether polymers, thermosetting polymers, polyimides, and polyimide-type engineering plastics, polyesters, silicone polymers, polyurethanes, and natural and cellulose-type polymers, none of which shared more than two common pyrolytic products with Unknown
Subsample A (Shin et al., 2011). Additional research is needed in this area of study, particularly that which investigates the potential for varying polymers to adsorb chemical and environmental pollutants. To the best of the authors' knowledge, pyr-GC/MS has not been previously utilized for microplastic polymer identification within a fish ingestion study. However, the polymer classes identified within the current study are similar to those in Neves et al. (2015), which utilized μ-FTIR to identify polypropylene, polyethylene, rayon, polyester, and nylon 6 within the stomach contents of fish off of the Portuguese coast, Lusher et al. (2013) which utilized FTIR to identify rayon and polyamide polymers within fish from the English Channel, and Possatto et al. (2011) which visually identified blue nylon fragments within marine catfish. Additionally, the results of this study are similar to that of Hendrickson et al. (2018), which identified PVC, PP, PE, and PET within microplastic particles in the surface waters of Lake Superior. However, Hendrickson et al. (2018) did not detect compounds similar to those of Unknown Subsample A, suggesting that this chemical configuration may be more prevalent within the Gulf of Mexico than in the Great Lakes and similar freshwater systems. Further research of petroleum or chemical processing byproducts, resulting in the formation of particles or potential absorption, would help to clarify this discrepancy and direct future microplastic polymer research in areas with high levels of local industrialization. Overall, the results of this study indicate that pyr-GC/MS is an applicable tool for microplastic polymer identification within a fish ingestion study. However, this method is limited by the following factors: 1. Size Constraint: Analysis success significantly decreases when particles are less than ten micrograms, thus the smallest microplastic size classes are underrepresented within the literature; 2. Time Constraint: Pyr-GC/MS is time intensive and requires a minimum of 45 min per particle for sample prep and analysis; 3. Monetary Constraint: It is difficult to obtain a representative pyr-GC/MS sample for studies that have large number of microplastic particles; and 4. Standard Constraint: There is a lack of comparative standard data available, especially that which represents pyrolytic products associated with polymers which have undergone environmental transformations. Future research is needed which addresses these limitations and focuses on the development of a more efficient and effective method for microplastic polymer identification. 5. Conclusions The quantification of microplastic polymer ingestion by fish is an important first step toward understanding the breadth of microplastic pollution throughout aquatic systems and potential adverse effects resulting from microplastic exposure via ingestion. This study confirms pyr-GC/MS as an applicable analytical tool for microplastic polymer identification and is one of the first to utilize pyr-GC/MS for polymer identification within a fish ingestion study. Overall, the polymer classes identified within this study are similar to that Hendrickson et al. (2018), however, unique to this study was the identification of particles hypothesized to be related to petroleum industrialization. Future research is needed which focuses on the development of polymer identification methods that maintain sample integrity, are not limited by particle size or type, and can be quickly and efficiently utilized for polymer identification. Funding sources This work was supported by the C. Gus Glasscock Jr. Endowed Fund for Excellence in Environmental Science. Acknowledgments The authors would like to thank the C. Gus Glasscock Jr. Endowed Fund for Excellence in Environmental Science for their funding support 94
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and the Usenko Laboratory (Baylor University) and Zach Winfield for the use of their NIST Library.
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References British Plastics Federation, 2018. Polyvinyl Chloride PVC. http://www.bpf.co.uk/ plastipedia/polymers/PVC.aspx. Dümichen, E., Barthel, A.-K., Braun, U., Bannick, C.G., Brand, K., Jekel, M., Senz, R., 2015. Analysis of polyethylene microplastics in environmental samples, using a thermal decomposition method. Water Res. 85, 451–457. Eriksen, M., Mason, S., Wilson, S., Box, C., Zellers, A., Edwards, W., Farley, H., Amato, S., 2013. Microplastic pollution in the surface waters of the Laurentian Great Lakes. Mar. Pollut. Bull. 77, 177–182. Eriksen, M., Lebreton, L.C.M., Carson, H.S., Thiel, M., Moore, C.J., Borerro, J.C., Galgani, F., Ryan, P.G., Reisser, J., 2014. Plastic pollution in the World's oceans: more than 5 trillion plastic pieces weighing over 250,000 tons afloat at sea. PLoS One 9 (12), 111913. Fardhyanti, D.S., Damayanti, A., 2015. Analysis of coal tar composition from sub-bituminous Kalimantan coal tar. Int. J. Chem. Mol. Nucl. Mater. Metall. Eng. 9, 32–42. Fisher, C.H., 1938. Composition of Coal Tar and Light Oil. United States Department of the Interior. Bureau of Mines. Frias, E., Dekiff, J.H., Willmeyer, J., Nuelle, M.-T., Ebert, M., Remy, D., 2013. Identification of polymer types and additives in marine microplastic particles using pyrolysis-GC/MS and scanning electron microscopy. Environ. Sci. Processes Impacts 15 (10), 1949–1956. Gewert, B., Plassmann, M., MacLeod, M., 2015. Pathways for degradation of plastic polymers floating in the marine environment. Environ. Sci. Processes Impacts 17 (9), 1513–1521. Hendrickson, E., Minor, E.C., Schreiner, K., 2018. Microplastic abundance and composition in Western Lake Superior as determined via microscopy, Pyr-GC/MS, and FTIR. Environ. Sci. Technol. 52 (4), 1787–1796. Hurley, R.R., Woodward, J.C., Rothwell, J.J., 2017. Ingestion of microplastics by freshwater tubifex worms. Environ. Sci. Technol. 51 (21), 12844–12851. Jiang, J., Wang, Q., Tong, W., Xiao, B., 2007. GC/MS analysis of coal tar composition produced from coal pyrolysis. Bull. Chem. Soc. Ethiop. 21 (2), 229–240. Kang, J.H., Kwon, O.Y., Lee, K.W., Song, Y.K., Shim, W.J., 2015. Marine neustonic microplastics around the southeastern coast of Korea. Mar. Pollut. Bull. 96, 304–312. Käppler, A., Fischer, D., Oberbeckmann, S., Schernewski, G., Labrenz, M., Eichhorn, K.J., Voit, B., 2016. Analysis of environmental microplastics by vibrational microspectroscopy: FTIR, Raman or both? Anal. Bioanal. Chem. 408 (29), 8377–8391. Lattin, G.L., Moore, C.J., Zellers, A.F., Moore, S.L., Weisberg, S.B., 2004. A comparison of neustonic plastic and zooplankton at different depths near the southern California shore. Mar. Pollut. Bull. 49 (4), 291–294. Law, K.L., Morét-Ferguson, S., Maximenko, N.A., Proskurowski, G., Peacock, E.E., Hafner, J., Reddy, C.M., 2010. Plastic accumulation in the North Atlantic subtropical gyre. Science 329 (5996), 1185–1188. Lehrle, R.S., Parsons, I.W., Rollinson, M., 2000. Thermal degradation mechanisms of Nylon 6 deduced from kinetic studies by pyrolysis-g.c. Polym. Degrad. Stab. 67, 21–33. Lenz, R., Enders, K., Stedmon, C.A., Makenzie, D.M.A., Nielsen, G.T., 2015. A critical assessment of visual identification of marine microplastic using Raman spectroscopy for analysis improvement. Mar. Pollut. Bull. 11 (1), 82–91. Löder, M.G.J., Gerdts, G., 2015. Methodology used for the detection and identification of microplastics—A critical appraisal. In: Bergmann, M., Gutow, L., Klages, M. (Eds.), Marine Anthropogenic Litter. Springer, Berlin, pp. 201–227. Lusher, A.L., McHugh, M., Thompson, R.C., 2013. Occurrence of microplastics in the gastrointestinal tract of pelagic and demersal fish from the English Channel. Mar. Pollut. Bull. 67, 94–99. Moore, C.J., Lattin, G.L., Zellers, A.F., 2011. Quantity and type of plastic debris flowing from two urban rivers to coastal waters and beaches of Southern California. J. Integr. Coast. Zone Manag. 11 (1), 65–73. Nadal, M.A., Alomar, C., Deudero, S., 2016. High levels of microplastic ingestion by the semipelagic fish bogue boops boops (L.) around the Balearic Islands. Environ. Pollut. 214, 517–523.
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Pyr-GC/MS analysis of microplastics extracted from the stomach content of benthivore fish from the Texas Gulf Coast
T
Colleen A. Petersa, , Erik Hendricksonb, Elizabeth C. Minorb, Kathryn Schreinerb, Julie Halburb, Susan P. Brattona ⁎
a b
Department of Environmental Science, Baylor University, Waco, TX 76798-7266, United States of America University of Minnesota, Duluth, United States of America
ARTICLE INFO
ABSTRACT
Keywords: Microplastic Polymer Pyr-GC/MS Fish Ingestion Texas
Fish ingestion of microplastic has been widely documented throughout freshwater, marine, and estuarine species. While numerous studies have quantified and characterized microplastic particles, analytical methods for polymer identification are limited. This study investigated the applicability of pyr-GC/MS for polymer identification of microplastics extracted from the stomach content of marine fish from the Texas Gulf Coast. A total of 43 microplastic particles were analyzed, inclusive of 30 fibers, 3 fragments, and 10 spheres. Polyvinyl chloride (PVC) and polyethylene terephthalate (PET) were the most commonly identified polymers (44.1%), followed by nylon (9.3%), silicone (2.3%), and epoxy resin (2.3%). Approximately 42% of samples could not be classified into a specific polymer class, due to a limited formation of pyrolytic products, low product abundance, or a lack of comparative standards. Diethyl phthalate, a known plasticizer, was found in 16.3% of the total sample, including PVC (14.3%), silicone (14.3%), nylon (14.3%), and sample unknowns (57.2%).
1. Introduction Microplastics are major global contaminants, ubiquitous throughout freshwater and marine systems (Eriksen et al., 2013; Lattin et al., 2004; Moore et al., 2011; Ng and Obbard, 2006; Sadri and Thompson, 2014). Due to their small size (i.e. < 5 mm), it is difficult to predict particle transport following release into aquatic systems, however, microplastics have been discovered within waters from the near shore to open ocean (Eriksen et al., 2014; Kang et al., 2015), from the surface to benthos (Song et al., 2014; Woodall et al., 2014), and from subtropical to polar seas (Law et al., 2010; Obbard et al., 2014). While the environmental impact of these contaminants is not fully understood, microplastic ingestion has been identified within taxa spanning from invertebrates to large marine mammals (Hurley et al., 2017; Taylor et al., 2016). Fish ingestion of microplastic has been confirmed within freshwater, marine, and estuarine species, ranging from a few percent to more than two-thirds of all fish examined (Lusher et al., 2013; Nadal et al., 2016; Peters and Bratton, 2016; Peters et al., 2017; Possatto et al., 2011; Romeo et al., 2015; Sanchez et al., 2014; Vendel et al., 2017). It is likely that variations in microplastic ingestion are the result of several factors, such as the species of examination, location of collection,
⁎
methodologies employed for microplastic extraction, and the analytical analyses utilized for polymer identification. Due to the complex nature of these micro-contaminants, it is now becoming standard to employ two or more identification techniques for the confirmation of plastic. Initial identification routinely involves a physical characterization of the particle (e.g. size, morphology, and color), aided by microscopy, followed by a secondary identification via chemical characterization (e.g. spectroscopy) to identify the specific type of plastic polymer (Shim et al., 2017). Polymer characterization often employs Fourier transform infrared spectroscopy (FTIR) or Raman spectroscopy (Lenz et al., 2015; Shim et al., 2017). Both techniques use electromagnetic radiation to profile samples, however, FTIR is a measure of the particle's covalent chemical bonds using the absorbance of their vibrational modes while Raman is a measure of the particle molecular structure using light scattering from key vibrational modes after excitation with a visible light source (Käppler et al., 2016; Löder and Gerdts, 2015). While both techniques explore molecular vibrations, the differences in their approach mean that different types of bonds are highlighted by each technique. FTIR spectra highlight polar covalent bonds, while Raman spectra highlight more purely covalent bonds such as carbon to carbon (CeC) or sulfur to sulfur (SeS). FTIR is coupled with varying modes of measure (e.g.
Corresponding author at: Department of Environmental Science, Baylor University, One Bear Place #97266, Waco, TX 76798-7266, United States of America. E-mail address: [email protected] (C.A. Peters).
https://doi.org/10.1016/j.marpolbul.2018.09.049 Received 7 June 2018; Received in revised form 28 September 2018; Accepted 29 September 2018 0025-326X/ © 2018 Elsevier Ltd. All rights reserved.
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micro-FTIR, transmission, reflectance, and attenuated total reflectance), some of which can minimize method limitations such as the requirement of an extensive sample pretreatment and inhibited analysis of plastics which contain irregular surfaces, (Ng and Obbard, 2006; Song et al., 2014). While FTIR can analyze particles as small as 10 μm (the size of the IR beam aperture), particles of this size often require multiple analysis runs or produce unclear results, thus FTIR is most applicable for particles that are > 50μm (Shim et al., 2017). Comparatively, the laser aperture utilized in Raman spectroscopy is smaller than that of FTIR, thus it can identify particles as small as a few μm in size. Raman has been found to be sensitive to additive and pigment chemicals, which are often incorporated during the production phase of plastics, resulting in the interference in polymer identification (Lenz et al., 2015). FTIR may also be challenged by additives and plastic copolymers; work by Hendrickson et al., 2018 indicates that ATR-FTIR may mask chemical constituents within heterogeneous particles that appear when the particle undergoes pyr-GC/MS analysis. A third method of polymer identification is the coupling of thermal desorption or pyrolysis with gas chromatography–mass spectrometry or GC/MS (Dümichen et al., 2015; Frias et al., 2013). Pyrolysis-GC/MS (pyr-GC/MS) uses heat in an inert environment (i.e., no oxygen) to decompose polymeric material in a predictable fashion. The pieces of polymer generated can then be separated by gas chromatography on the basis of their size and polarity and analyzed by mass spectrometric detector at the outlet of the gas chromatography column. (Frias et al., 2013). This method yields a total chromatogram (abundance vs time) for the separated pyrolysis products and provides mass spectrometry data throughout the chromatogram as well. These can be compared against a known reference library to determine the specific class of polymer being analyzed. This method is beneficial over FTIR and Raman spectroscopy as it can characterize particles < 10μg when measured in splitless mode, and the utilization of a thermal analysis combined with GC/MS, enables the separation and analysis of chemical additives as well as the polymer material (Hendrickson et al., 2018). However, pyr-GC/MS is destructive, resulting in the total loss of the particle and subsequently eliminating further particle analysis. Despite recent advancements in these analytical methods as applied to microplastics, the applicability and feasibility of each is somewhat incomplete due to the wide range of microplastic polymers, including weathered polymers, and additives found throughout the environment. This research serves as one of the first applications of pyr-GC/MS for microplastic polymer identification within a fish ingestion study, and specifically investigates the polymer distribution of microplastic recovered from the stomach content of six marine fish species from the Texas Gulf Coast. The pyr-GC/MS method utilized within this study has previously been applied to the identification of microplastic recovered from the waters of Western Lake Superior (Hendrickson et al., 2018). The use of this method here enabled a comparison of polymer results between freshwater and marine systems and an investigation of method applicability across sample matrixes.
microbalance and particles < 10 μg were measured in splitless introduction into the gas chromatograph, while particles > 10 μg were introduced using a 1:100 split (Hendrickson et al., 2018). Samples were analyzed using an Agilent 7890B Gas Chromatograph with Agilent 5977A mass-selective detector (MSD) Mass Spectrometer and Gerstel Pyrolysis/Thermal Desorption Unit (Gerstel GmbH & Co. KG, Germany). All pyrolyzer and GC unit parameters adhered to the protocol of Hendrickson et al. (2018). The MSD utilized electron impact (EI+, 70 eV) for the ionization source and scanned for ions from m/z 10–550 (Hendrickson et al., 2018). Following analysis, ion chromatograms were assessed with the National Institute of Standards and Technology (NIST) mass spectra library (Version 2.0, 12/4/12, available through the mass spectrometer's software package) and the following standards: medium-density polyethylene (MDPE, catalog #: EV306010), polystyrene (PS, catalog #: ST316051), polyvinyl chloride (PVC, catalog #: CV316010), and polyethylene terephthalate (PET, catalog #: ES306030), all in powder form (250–350 μm) (Goodfellow, Inc.). Samples which yielded a low number of pyrolytic products (< 4 total) or low pyrolytic product abundances were evaluated via Mass Hunter qualitative analysis software which was utilized to integrate total ion chromatogram peak areas and calculate a 3:1 signal-to-noise ratio (Hendrickson et al., 2018). 3. Results A total of 43 microplastic samples were analyzed, inclusive of 30 fibers, 3 fragments, and 10 spheres (microbeads). Particles were identified into the following five polymer classes: PVC (Fig. 1; Table 1) and PET, constituting approximately 44.1% of the total sample, silicone (2.3%), epoxy resin (2.3%), and nylon (9.3%) (Fig. 2). Half of the nylon particles were further classified as Nylon 6 due to the high abundance of caprolactam within the pyrogram results (Lehrle et al., 2000). In addition to the five polymer classes, approximately 42% of particles were classified as sample unknowns, 21% of which displayed a similar chromatogram result inclusive of seven common pyrolytic products (i.e. Unknown Subsample A) (Fig. 3; Table 2). PVC polymers were inclusive of microplastic fibers (73.3%), fragments (20.0%), and spheres (6.7%), while all PET, epoxy resin, and nylon polymers were in the form of microplastic fibers and the single particle identified as silicone was in the form of a microbead. Particles classified as “Unknown” contained fiber (55.6%) and sphere (44.4%) morphologies (Fig. 4) and particles further categorized as “Unknown Subsample A” contained sphere (55.6%) and fiber (45.4%) morphologies. Diethyl phthalate was found in 16.3% of all particles analyzed, including PVC (14.3%), Silicone (14.3%), Nylon (14.3%), Unknown (28.6%), and Unknown Subsample A (28.6%). 4. Discussion Of the original marine microplastic data set (i.e. Peters et al., 2017), the most common particle morphologies were fibers (86.4%), followed by spheres (12.9%), and fragments (< 1.0%), thus, samples chosen for
2. Methods Microplastics were collected from the stomach content of 1381 marine fish, inclusive of six species (i.e. southern kingfish (Menticirrhus americanus), Atlantic croaker (Micropogonias undulates), Atlantic spadefish (Chaetodipterus faber), sand trout (Cynoscion arenarius), pinfish (Lagodon rhomboids), and grunt (Orthopristis chrysoptera) from the Texas Gulf Coast (Peters et al., 2017). Fish collection took place from September 2014 to September 2015, and stomach content analysis followed the protocol of Peters and Bratton (2016). Following identification, microplastics were characterized via particle size, morphology, and color (Peters et al., 2017). Approximately 5% of recovered microplastics were selected and transferred from Baylor to the University of Minnesota Duluth for pyrGC/MS analysis. Particle mass was measured via a Mettler Toledo XP2U
25000000 4.
Abundance
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6.
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8. 9.
4.0 4.7 5.5 6.2 6.9 7.6 8.4 9.1 9.8 10.5 11.3 12.0 12.7 13.4 14.2 14.9 15.6 16.3 17.1 17.8 18.5 19.2 20.0 20.7 21.4
0 Retention Time
Fig. 1. Total chromatogram of PVC particle with pyrolytic products numerically labeled. 92
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Table 1 PVC pyrolytic products with retention times and parent m/z's. Sample Peak
Retention Time ( ± 0.5 min)
Pyrolytic Product
Parent m/z
1 2 3 4 5 6 7 8 9
4.4 6.5 7.2 9.5 12.1 13.1 14.8 16.9 20.2
Styrene Mesitylene Indene Naphthalene Biphenyl Acenaphthylene Diethyl phthalate Anthracene Pyrene
104 120 116 128 154 154 222 178 202
Fig. 4. Distribution of particle polymer classes and associated morphologies.
for analysis and the comparatively low proportion of microplastic fibers sampled. Previously, microplastic form has been utilized to hypothesize originating sources of pollution (e.g. fibers resulting from wastewater effluent), however, due to varying environmental conditions and a limited knowledge of microplastic transport pathways, it is difficult to assign microplastics to a single source. While pyr-GC/MS cannot distinguish originating sources of pollution, it can identify specific polymers and subsequently a measure of polymer distribution throughout the sample matrix. Polymer identification is also a first step toward predicting ultimate fate in an aquatic environment; for example, different plastic polymers have different susceptibilities to various biologically or photochemically mediated degradation processes (Gewert et al., 2015). Of the five polymer classes identified, PVC and PET were the most common, while all other polymer classes collectively constituted < 15% of the total sample. PVC is one of the leading polymers worldwide, accounting for 20% of all plastics manufacturing and prominent within applications for building, transport, and packaging, and as a strengthening coating for fabrics (British Plastics Federation, 2018). PET is the most common thermoplastic polymer resin of the polyester family and is widely utilized for packaging and bottle applications and for the production of synthetic fibers (i.e. polyester). Both PVC and PET are high density polymers (approximately 1.38 g/cm3), thus they are likely to sink within freshwater and marine habitats. As the fish species originally examined are benthivore foragers, the high proportion of PVC and PET within the sample is not unexpected. However, a study of microplastic pollution within Western Lake Superior identified PVC as the most common microplastic polymer collected within surface water samples and PET as the 4th most commonly identified polymer, suggesting that polymer density does not ultimately determine particle fate and transport (Hendrickson et al., 2018). In total, 42% of all particles analyzed could not be classified into a specific polymer class, due to either a lack of pyrolytic products formed, low pyrolytic product abundance, or the lack of a clear polymer match, and were subsequently categorized as “Unknown”. Fifty percent of unknown particles displayed a similar pyrogram result, suggesting a shared compositional origin, and were subsequently separately classified (i.e. Unknown Subsample A) despite the lack of polymer match. The particles classified as Unknown Subsample A were not cohesive in morphology, containing both spheres and fibers, which indicates a variation in origin. Additionally, half of the fibers within Unknown Subsample A exhibited diethyl phthalate, a known plasticizer, as a pyrolytic product. One possible hypothesis is that the particles of Unknown subsample A are associated with the petroleum industry (e.g. manufacturing waste or pitch byproducts or perhaps even products from plastics manufacturing, whose feedstock is usually from petroleum). The petroleum industry is highly concentrated along the Texas Gulf Coast and reflective of local land use patterns, such as high levels of local petroleum and natural gas industrialization and major petroleum transport zones (i.e. Port of Galveston). It is possible that particles with a sphere morphology are a direct result of the petroleum industry, while the fiber particles were in fact known plastic polymers which had
Fig. 2. Distribution of polymer classes identified via pyr-GC/MS. 6.
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Abundance
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. 8.
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0
Retention Time
Fig. 3. Total chromatogram of Unknown Subsample A, a fiber particle with pyrolytic products numerically labeled. Table 2 Unknown Subsample A pyrolytic products with retention times and parent m/ z's. Pyrolytic products of peaks 1–7 are indicative of the particles within this classification. Peak number
Retention time ( ± 0.5 min)
Pyrolytic product
Parent m/z
1 2 3 4 5 6 7 8
6.2 7.7 8.5 8.7 10.1 10.9 12.2 14.7
Phenol 4‑Methylphenol 2,5‑Pyrrolidinedione Benzonitrile Benzenepropanenitrile Indole 3‑Methylindole Diethyl Phthalate
94 108 99 103 131 117 131 222
pyr-GC/MS analysis proportionally favored sphere and fragment morphologies, which were on average larger than microplastic fibers (Peters et al., 2017). Overall, the total number of samples analyzed was limited by time and monetary constraints, while the particles chosen for analysis were also limited by a minimum size constraint. These three factors contributed to the distribution of particle morphologies chosen 93
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been coated in or had absorbed waste products, subsequently inhibiting polymer identification. In addition to a high concentration of petroleum industrialization, major pollution events, such as the British Petroleum Deepwater Horizon oil spill, have impacted the Texas Gulf Coast (Nelson et al., 2015; Singleton et al., 2016). Studies investigating the impact of events, such as oil spills or petroleum waste contamination, cite inhalation, aspiration, ingestion via contaminated sediment, water, or prey, and absorption through the skin, as routes of exposure to aquatic organisms (NOAA, 2017). Laboratory studies investigating potential adverse effects, via the exposure of fish to oil contaminated sediment, indicate that fish experienced reduced growth (i.e. length and weight) and fecundity (Raimondo et al., 2016). Fish exposure to oil via contaminated water has also been linked to decreased swimming speeds, maximum metabolic rate, and aerobic scope (Stieglitz et al., 2016). While Peters et al. (2017) did not investigate adverse effects, the hypothesis of this study suggests that Texas Gulf Coast fish may experience adverse effects resulting from the ingestion of petroleum contaminated particles. A secondary hypothesis is that the particles of Unknown Subsample A are associated with coal tar, which is a liquid oil byproduct of coal pyrolysis and is utilized in its raw form for industries such as synthetic fiber, dyestuff, and coatings (Fardhyanti and Damayanti, 2015; Jiang et al., 2007). Major coal tar compound groups include: oxygen containing compounds (e.g. phenols), nitrogen containing compounds (e.g. amines), aromatic hydrocarbons (e.g. benzene hydrocarbons), and inorganic constituents (Fisher, 1938). Of the seven pyrolytic products indicative of Unknown Subsample A, phenol and 4-methylphenol (i.e. phenol group), and indole, 3-methylindole, and benzonitrile (i.e. amines) have been identified as constituents of coal tar. Additionally, diethyl phthalate was identified as a product of coal tar pyrolysis, suggesting that its presence does not always indicate a parent plastic polymer (Jiang et al., 2007). One primary use of coal tar is for the production of coal tar creosote (i.e. creosote), formed via the fractional distillation of crude coal tar (World Health Organization, 2004). Creosote is an oily liquid, consisting of aromatic hydrocarbons, anthracene, naphthalene, and phenanthrene derivatives and comprised of approximately 85% PAH's and 2–17% phenolics (U.S. Department of Health and Human Services, 2002). Similar to both coal tar and petroleum waste, creosote is a multicomponent mixture that varies per the source and preparation parameters utilized for production. Because of this, creosote components are rarely consistent per collective type and relative concentrations, however, display similar compositional chemical groups as that of coal tar. Creosote is utilized along the Texas Gulf coast as a wood preservative and water-proofing agent for marine pilings and as a preventative agent for plant and animal growth along concrete marine pilings (U.S. Department of Health and Human Services, 2002). Because of this, and similar to previous petroleum and coal tar hypotheses, creosote may serve as a vector for chemical pollution throughout Texas Gulf Coast waters and subsequently affect microplastic polymer identification. Although the hypotheses associating Unknown Subsample A with petroleum waste, coal tar, and creosote are preliminary, extensive research was conducted to investigate probable alternatives and none were readily identified. In addition to a literature search, the sample chromatogram results were compared against a NIST polymer library, and a reference text polymer library, inclusive of 163 standard polymer samples (Shin et al., 2011). Major groups represented within the library include: polyolefins, vinyl polymers with ethylene units, vinyl polymers with styrene units, vinyl polymers with styrene derivatives, acrylatetype polymers, chlorine-containing vinyl polymers, fluorine-containing vinyl polymers, diene-type elastomers, polyamides, polyacetals and polyether polymers, thermosetting polymers, polyimides, and polyimide-type engineering plastics, polyesters, silicone polymers, polyurethanes, and natural and cellulose-type polymers, none of which shared more than two common pyrolytic products with Unknown
Subsample A (Shin et al., 2011). Additional research is needed in this area of study, particularly that which investigates the potential for varying polymers to adsorb chemical and environmental pollutants. To the best of the authors' knowledge, pyr-GC/MS has not been previously utilized for microplastic polymer identification within a fish ingestion study. However, the polymer classes identified within the current study are similar to those in Neves et al. (2015), which utilized μ-FTIR to identify polypropylene, polyethylene, rayon, polyester, and nylon 6 within the stomach contents of fish off of the Portuguese coast, Lusher et al. (2013) which utilized FTIR to identify rayon and polyamide polymers within fish from the English Channel, and Possatto et al. (2011) which visually identified blue nylon fragments within marine catfish. Additionally, the results of this study are similar to that of Hendrickson et al. (2018), which identified PVC, PP, PE, and PET within microplastic particles in the surface waters of Lake Superior. However, Hendrickson et al. (2018) did not detect compounds similar to those of Unknown Subsample A, suggesting that this chemical configuration may be more prevalent within the Gulf of Mexico than in the Great Lakes and similar freshwater systems. Further research of petroleum or chemical processing byproducts, resulting in the formation of particles or potential absorption, would help to clarify this discrepancy and direct future microplastic polymer research in areas with high levels of local industrialization. Overall, the results of this study indicate that pyr-GC/MS is an applicable tool for microplastic polymer identification within a fish ingestion study. However, this method is limited by the following factors: 1. Size Constraint: Analysis success significantly decreases when particles are less than ten micrograms, thus the smallest microplastic size classes are underrepresented within the literature; 2. Time Constraint: Pyr-GC/MS is time intensive and requires a minimum of 45 min per particle for sample prep and analysis; 3. Monetary Constraint: It is difficult to obtain a representative pyr-GC/MS sample for studies that have large number of microplastic particles; and 4. Standard Constraint: There is a lack of comparative standard data available, especially that which represents pyrolytic products associated with polymers which have undergone environmental transformations. Future research is needed which addresses these limitations and focuses on the development of a more efficient and effective method for microplastic polymer identification. 5. Conclusions The quantification of microplastic polymer ingestion by fish is an important first step toward understanding the breadth of microplastic pollution throughout aquatic systems and potential adverse effects resulting from microplastic exposure via ingestion. This study confirms pyr-GC/MS as an applicable analytical tool for microplastic polymer identification and is one of the first to utilize pyr-GC/MS for polymer identification within a fish ingestion study. Overall, the polymer classes identified within this study are similar to that Hendrickson et al. (2018), however, unique to this study was the identification of particles hypothesized to be related to petroleum industrialization. Future research is needed which focuses on the development of polymer identification methods that maintain sample integrity, are not limited by particle size or type, and can be quickly and efficiently utilized for polymer identification. Funding sources This work was supported by the C. Gus Glasscock Jr. Endowed Fund for Excellence in Environmental Science. Acknowledgments The authors would like to thank the C. Gus Glasscock Jr. Endowed Fund for Excellence in Environmental Science for their funding support 94
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and the Usenko Laboratory (Baylor University) and Zach Winfield for the use of their NIST Library.
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